Object identification using radar data

ABSTRACT

An evaluation device for obtaining a segmentation of an environment from a radar recording of the environment, that has an input interface configured to obtain initial training data, where the initial training data comprise radar data of the radar recording and initial characteristics of objects located in the environment recorded with a radar sensor that generates the radar recordings, and where the evaluation device is configured to forward propagate an artificial neural network with the initial training data to obtain second characteristics of the objects determined with the artificial neural network in the forward propagation, and to obtain weighting factors for neural connections of the artificial neural network through backward propagation of the artificial neural network with the differences between the second characteristics and the initial characteristics, in order to obtain the segmentation of the environment through renewed forward propagation with these radar data.

RELATED APPLICATIONS

This application claims the benefit and priority of German PatentApplication DE 10 2018 203 684.5, filed Mar. 12, 2018, which isincorporated by reference herein in its entirety.

TECHNICAL FIELD

The present disclosure relates to an evaluation device for obtaining asegmentation of a radar recording and a related method. Moreover, thepresent disclosure relates to a training system for obtaining asegmentation of a radar recording. The present disclosure also relatesto an application system for a street vehicle for segmenting radarrecordings. The present disclosure also relates to a radar sensor in astreet vehicle with an application system. The present disclosure alsorelates to a use of an application system, a radar sensor, and a driverassistance system.

BACKGROUND

Radar systems often contain a radar sensor and an assembly oftransmitting and receiving antennas. Local maxima are searched for withknown radar systems in a range-Doppler map, which is obtained with pairsof transmitting and receiving antennas in the radar system. The localmaxima are searched for with a false alarm rate algorithm, referred toin English as a “constant false alarm rate.” The constant false alarmrate indicates the average number of false targets, e.g. from backgroundnoise. It is possible to determine with a threshold value the signalamplitude, starting at which a signal is indicated as a target. Signalswith an amplitude lying below this threshold value are discarded asnoise signals. The threshold value is adjusted adaptively with theconstant false alarm rate based on the state of the environment.

Threshold values must be set high enough to discard recorded butundesired targets, referred to in English as clutter, such that onlyrelevant targets are examined. Other information is also lost thereby.With 24 pairs of transmitting and receiving antennas, for example, eachof which has a distance and speed resolution of 1024×512, thus more than12 million values, usually only a few hundred maxima are selected, whichare then processed. The rest are discarded.

With a false alarm rate algorithm, a majority of the information fromthe original range-Doppler map is not used. By way of example, none ofthe information regarding detailed characteristics of the maxima is usedin the signal processing. Neither is information regarding the spatialdistribution of the targets, nor information regarding an implicitantenna characteristic of the radar system, used. Only the signal/noisevalue for each maximum continues to be used. Global connections fromwhich a global scenario can be derived are not taken into account.

Characterization of a finite number of video sequences, complete withfeatures, e.g. vehicles, pedestrians, pathways, streets, signs, is knownfrom video-based scenario segmentation. Characterization is alsoreferred to as labeling. By labeling, it is possible to assign a meaningto the recorded object based on the raw pixel data obtained from theimaging system. An artificial neural network is trained with recordingslabeled accordingly, to obtain object characteristics, e.g. dimensionsand/or colors, as well as spatial relations of the objects to oneanother, e.g. that there is a street underneath the vehicle, and thereare trees and signs next to the street. An artificial neural networkthat obtains a semantic segmentation of images in real time is disclosedin arXiv:1704.08545.

In view of the above, an object of the present disclosure is to providea semantic segmentation of radar recordings of an environment, in orderto obtain a global scenario of these environment.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure shall be explained in greater detail below, basedon the following figures.

FIG. 1 shows an exemplary embodiment of an evaluation device accordingto the present disclosure.

FIG. 2 shows an exemplary embodiment of an antenna assembly for a radarsystem with two transmitting antennas and four receiving antennas.

FIG. 2b shows the transmitting and receiving antenna paths from FIG. 2a.

FIG. 3 shows an exemplary embodiment of the interference signalsobtained in the paths in FIG. 2 b.

FIG. 4 shows an exemplary embodiment of a training system according tothe present disclosure.

FIG. 5 shows an exemplary embodiment of a distance/speed distribution.

FIG. 6 shows an exemplary embodiment for beamforming.

FIG. 7 shows an exemplary embodiment of a data range according to thepresent disclosure.

FIG. 8 shows an exemplary embodiment of an application system accordingto the present disclosure.

FIG. 9 shows a schematic illustration of the method according to thepresent disclosure.

FIG. 10 shows a schematic illustration of the training process accordingto the present disclosure.

DETAILED DESCRIPTION

As stated above, one object of the present disclosure is to provide asemantic segmentation of radar recordings of an environment, in order toobtain a global scenario of this environment. Without limitation, theobject may be achieved by an evaluation device for obtaining asegmentation of a radar recording of an environment, a related methodfor obtaining radar data, a training system for obtaining a segmentationof a radar recording of an environment, a training process for obtaininga segmentation of a radar recording of an environment, an applicationsystem for a street vehicle for segmenting radar recordings, a radarsensor for a street vehicle with an application system, and/or a use ofan application system, a radar sensor, or a driver assistance system.

For example: a radar recording of an environment is segmented with theevaluation device according to the present disclosure. The evaluationdevice has an input interface that is configured to obtain initialtraining data. The initial training data comprise radar data from theradar recording and initial characterizations of the objects located inthe environment recorded by a radar sensor, which generates the radarrecordings. The radar data contain the respective distances of theseobjects to the radar sensor, and the respective angles between theobjects and the radar sensor belonging to these distances. Theevaluation device is configured to forward propagate an artificialneural network with the initial training data. The evaluation device isconfigured to obtain second characteristics of the objects determined inthe forward propagation of the artificial neural network. The evaluationdevice is also configured to obtain weighting factors for neuralconnections of the artificial neural network through backwardpropagation of the artificial neural network with the differencesbetween the second characteristics and initial characteristics. In thismanner, the environment is segmented with a renewed forward propagationwith these radar data.

The following definitions apply to the entire subject matter of thepresent disclosure.

An evaluation device is a device that processes input information andoutputs a result based on this processing. In particular, an evaluationdevice is an electronic circuit, e.g. a central processing unit, or agraphics processor.

Radar is an abbreviation for “Radio Detection and Ranging” and refers todetection and location processes based on electromagnetic waves in theradio frequency range. The radio frequency range comprises frequenciesbelow 3000 GHz, thus long-wave frequencies of 30 kHz or more, mediumwaves, short waves, and ultrashort waves of up to 300 MHz. A radarsensor is used with a camera sensor or a lidar sensor as an environmentdetection sensor in a driver assistance system.

Segmentation is a subcategory of digital image processing, and machinevision. Segmenting means the generation of related regions according tospecific homogeneity criteria.

An interface is a device between at least two functional units, where anexchange of logical values, e.g. data or physical values, e.g. electricsignals, takes place, in either only one direction or bidirectionally.The exchange can be analog or digital. The exchange can be wireless orhard-wired. An interface can exist between software and software,hardware and hardware, and software and hardware and hardware andsoftware.

Training data is data with which a learning mechanism, e.g. anartificial neural network, learns information. Training data can be dataobtained with the vehicle during a training drive for an artificialneural network in a driver assistance system of a vehicle. Training dataare also simulated data. Initial training data are target training data,i.e. positive training data, with which the learning mechanism learnsreal information.

Second training data are error training data, e.g. error characteristicsof objects. The error characteristics each contain a correctioncharacteristic, in order to obtain an initial characteristic from theerror characteristic. Error training data are negative training data, bymeans of which the learning mechanism learns to respond to an error. Theevaluation device is preferably configured to forward propagate theartificial neural network with second training data. The second trainingdata are obtained via the input interface or a separate second inputinterface.

Initial characteristics are characteristics of an object thatcharacterize the object. The initial characteristics are the objective,real features of an object. The initial characteristics can bedetermined with an optical system, e.g. a camera, and correspond to thecharacteristics that can be perceived with this optical system. Theinitial characteristics are also referred to as target characteristics.By way of example, initial characteristics are the geometric dimensionsand/or colors of an object. Initial characteristics of a fire truckcomprise, e.g., the length, height, and width of the vehicle, and thecolor red. The initial characteristics preferably also obtaininformation regarding a relation to a second object, e.g. an opposingarrangement and/or distance.

Second characteristics are characteristics of an object determined forthis object by a mechanism. The second characteristics are also referredto as actual characteristics. An actual characteristic is thecharacteristic that the learning mechanism obtains after processing thetarget information, normally through computing, such that it is output.At the start of the training phase, the obtained actual characteristicnormally differs from the input target characteristic. The differencebetween the actual and the target characteristics is minimized, normallyaccording to the least squares method. After the training phase, thelearning mechanism, e.g. the artificial neural network, has concludedthe training phase, and is trained. The obtained actual characteristicsare nearly identical to the target characteristics in a trainedartificial neural network.

The initial image characteristics are preferably automatically obtainedwith an imaging sensor and one of the known methods for semantic imagesegmenting. Semantic image segmenting refers to the classification ofobjects in an image with classifiers. The imaging sensor preferablyexecutes an algorithm for pattern recognition, i.e. the algorithm is aclassifier. Patterns are recognized with special functions and/ortransformations, which map a feature space with numerousclassifications. By way of example, geometric figures are recognized bymeans of a Hough transformation. Advantageously, the classifier is anartificial neural network.

An artificial neural network is an algorithm that is executed on anelectronic circuit, and is programed using the neural network of thehuman brain as a model. Functional units of an artificial neural networkare artificial neurons, the output of which is given in general as avalue of an activation function, evaluated via a weighted sum of theinputs plus a systematic error, the so-called bias. By testing numerouspredetermined inputs with various weighting factors and/or activationfunctions, artificial neural networks are trained in a manner similar tothat of the human brain. The training of an artificial neural networkwith the aid of predetermined inputs, i.e. training data, is referred toas machine learning. A portion of the machine learning is the so-calleddeep learning, in which a series of hierarchical layers of neurons,so-called hidden layers, are used for executing the process of machinelearning. An artificial neural network with hidden layers is a deepneural network. A trained artificial neural network is distinguished byuseful reactions to new information. Examples of artificial neuralnetworks are perceptrons, and convolutional or recurrent neuralnetworks. Connections between neurons are evaluated with weightingfactors. Forward propagation means that information is supplied to theinput layer of the artificial neural network, passes through thesubsequent layers, and is output at the output layer. Backwardpropagation means that information is supplied to the output layer andoutput at the input layer. The errors of the respective layers areobtained by successive backward propagation of the error from the outputlayer to the respective preceding layer, until reaching the input layer.The errors are a function of the weighting factors. The weightingfactors are modified by minimizing the errors in the training phase. Asa result, when the inputs are input again, the desired output isapproximated. The backward propagation is described comprehensively inMichael A. Nielsen, Neural Networks and Deep Learning, DeterminationPress, 2015.

A convolutional artificial neural network is preferred. With this typeof network, a relatively small filter kernel is placed on a large inputfeature image. The activation of each neuron is computed via a discreteconvolution with this filter kernel and/or maximum pooling. The localmaximum is exploited with maximum pooling from a range of input featureimages.

As a result, the evaluation device obtains a segmented scenario radarrecording from a radar recording of an environment. A segmented scenariomeans that a three dimensional description of objects is obtained. Theartificial neural network learns to recognize structures in the radardata belonging to the objects in the environment, and to map thesestructures onto the objects and form a relationship to these objects.

The evaluation device is preferably configured to obtain a virtual imageof the environment from the radar recording with the artificial neuralnetwork, based on the initial characteristics and the segmentation.

The image obtained with the radar data is a virtual image of theenvironment, comparable to a virtual camera image. This image is not areal image from an optical system. Real objects in the environment arefirst indicated in the radar data as corresponding structures. Theartificial neural network learns to recognize these structures in theradar data. The artificial neural network learns by means of the initialcharacteristic to map these structures onto objects, and form arelationship to these objects. As a result, the artificial neuralnetwork obtains a virtual image of the environment from a radarrecording of the environment. The plausibility of real image from anoptical system, e.g. a camera, can be confirmed with this virtual image.

Advantageously, the initial characteristics are characteristics ofstreets, objects located on streets, preferably trees, vehicles, people,traffic signs, and/or vehicle restraint systems, preferably crashbarriers. In particular, classifications of vehicles can be recognizedbased on the radar data.

An example method according to the present disclosure for obtainingradar data comprises the following steps: outputting modulatedtransmitted signals, obtaining interference signals based on frequencymodulation and distances and relative speeds of objects in relation tothe radar sensor, against which the transmitted signals are reflected,wherein the interference signals are each composed of a reflected signalinterfering with at least one of the transmitted signals, obtaining adistance/speed distribution for the object based on the interferencesignals, determining azimuth and elevation angles between interferencesignals that arrive at the radar sensor from a direction defined by theazimuth and elevation angles, and the radar sensor, for each value ofthe distance/speed distribution, and obtaining radar data in the form ofa four dimensional data range containing the following dimensions:distance, relative speed, azimuth angle and elevation angle.

The transmitted signals are preferably frequency modulated.

The transmitted signals are preferably modulated with the frequencymodulated continuous wave method, and are signals that are emitted inquick sequences with a rising frequency ramp. The frequency modulatedcontinuous wave method is disclosed, e.g., in A. G. Stove, Linear FMCWradar techniques, in IEE Proceedings F-Radar and Signal Processing, vol.139, no. 5. pp. 343-350, 1992.

Such a transmitted signal s_(TX)(t) can be expressed as:

s _(TX)(t)=A ^(TX) sin(2π(f ₀ t+½αt ²)+φ₀).

The index TX stands for “transmitted.” A_(TX) is the amplitude of thetransmitted signal s_(TX)(t). f₀ is the initial frequency of thefrequency modulation. α is the frequency modulation rate, referred to inEnglish as the “chirp.” Φ₀ is the phase at the initial point in timet=0.

A reflected signal s_(RX)(t) is a signal that is reflected by an object.Such a reflected signal s_(RX)(t) can be expressed as:

s _(RX)(t)=A _(RX) sin(2π(f ₀(t−τ)+½α(t−τ)²)+φ₀+φ_(target)).

The index RX stands for “reflected.” A_(RX) is the amplitude of thereflected signal S_(RX)(t). Φ_(target) is a phase shift caused by atarget. τ is the time of flight for the reflected signal, which, due tothe Doppler shift, results in:

${\tau (t)} = {\frac{2}{c_{0}}\left( {r + {vt}} \right)}$

c₀ is the speed of light. r is the distance of the object to the radarsensor. v is the relative speed of the object to the radar sensor.

The interference signal s_(IF)(t) comprising the transmitted signals_(TX)(t) and the reflected signal s_(RX)(t) can be expressed asfollows, disregarding Φ_(target):

s _(IF)(t)=A _(IF) cos(2π(f ₀ τ+αtτ−½αt ²)).

The index IF stands for intermediate frequency. A_(IF) is the amplitudeof the interference signal s_(IF)(t). Using the relation for the flightof time t of the reflected signal s_(RX)(t) and assuming that therelative speed v is much lower than the speed of light c₀, the frequencyf_(IF) of the interference signal can be expressed as:

${f_{IF}(t)} = {{2\alpha \frac{r}{c_{0}}} + {2f_{0}\frac{v}{c_{0}}} + {2\alpha \; t{\frac{v}{c_{0}}.}}}$

The frequency f_(IF) of the interference signal s_(IF)(t) is thus afunction of the distance r to the object, its relative speed v to theradar sensor, and the frequency modulation rate α.

A distance/speed distribution is thus obtained for each pair oftransmitting and receiving antennas, which is referred to in English asthe “range-Doppler map.”

A substantial aspect of the present disclosure is that the directionfrom which the interference signal arrives is determined for each valueof the distance/speed distribution, i.e. for each point on therange-Doppler map from all of the transmitting/receiving antenna pairs.In contrast to known methods, not only are local maxima searched for bymeans of a constant false alarm rate algorithm, but also all of theinformation in the range-Doppler map is exploited. This results in afour dimensional data range, comprising the following dimensions:distance r, speed v, azimuth angle and elevation angle. This data rangecontains all of the maxima and the shapes and relations to one anotherthereof. It is substantial to the present disclosure that the artificialneural network learns to recognize structures in this data range, andforms a relationship to these objects by means of a training phase withobjects. The artificial neural network is thus configured to translatethe radar data, distance, speed, azimuth angle and elevation angle intoobject data.

The azimuth angle is the angle between a first reference direction of acoordinate system and the orthogonal projection of the pathway betweenthe object and the coordinate origin in the plane spanned by the firstreference direction and a second reference direction, orthogonal to thefirst reference direction. The azimuth angle ranges from zero to 2π.

The elevation angle is the angle between the polar direction and thepathway between the object and the coordinate origin. The elevationangle ranges from 0 to π.

A complete crossing of the azimuth angle and the elevation angle resultsin a spherical surface with a given radius.

Angles between objects at a distance r and the radar sensor aredetermined with the “direction of arrival” method, based on the factthat received signals from receiving antennas of an antenna assemblyexhibit a phase difference. One example of a direction of arrivalalgorithm is the known beamforming, in which the sum of all receivedsignals is formed with the corresponding phase corrections for a gridcomprising all of the possible angles. As a result, numerous targetobjects can be recognized at the same distance r to the radar sensor,and at the same speed v, but with different directions of arrival. It isalready possible with a two dimensional arrangement of the receivingantennas to determine the direction of an object by evaluating thedifferences in running lengths and the phase shifts of the receivedsignals, and consequently, together with the distance r, to determinethe exact three dimensional position of the object.

The radar data obtained with the evaluation device according to thepresent disclosure are preferably the radar data obtained in accordancewith the method according to the present disclosure.

The training system according to the present disclosure for obtaining asegmentation of a radar recording of an environment comprises a radarsensor with a receiving antenna assembly. The receiving antennas areconfigured to receive signals received from objects located in theenvironment that reflect transmitted signals. Furthermore, the trainingsystem has at least one imaging sensor, which is configured to obtaininitial characteristics of the object based on an image segmentation ofthe environment. Furthermore, the training system has an evaluationdevice, which is configured to forward propagate an artificial neuralnetwork with radar data from interference signals comprising respectivereflected signals and transmitted signals and the initialcharacteristics, wherein the radar data comprise the respectivedistances of these objects to the radar sensor, and the respectiveangles between the objects and the radar sensor belonging to thesedistances. The evaluation device is configured to obtain secondcharacteristics of the objects determined by the artificial neuralnetwork in the forward propagation. Moreover, the evaluation device isconfigured to obtain weighting factors for neural connections of theartificial neural network through backward propagation of the artificialneural network with the differences between the second characteristicsand the initial characteristics.

The imaging sensor is preferably a camera incorporated in a streetvehicle, with which initial training data are automatically generatedthrough semantic image segmenting while driving the street vehicle. Itis also within the scope of the present disclosure to automaticallygenerate initial training data with a lidar sensor or a stereo camera,in order to obtain three dimensional training data.

As a result, when the forward propagation with these radar data isrepeated, the segmentation of the surrounding is obtained. As a result,a training system for an artificial neural network is obtained, forlearning to recognize structures in radar data, and to allocate asemantic meaning of the corresponding object to these structures. Theartificial neural network learns, for example, to determine the positionof a vehicle and its relative movement based on distance, azimuth angle,and elevation angle, on the basis of initial imaging characteristics ofa vehicle.

Advantageously, a training system is used for executing the trainingprocess.

The training process according to the present disclosure for obtaining asegmentation of a radar recording of an environment may include thefollowing steps: obtaining initial training data, wherein the initialtraining data comprise radar data of the radar recording and initialcharacterizations of objects located in the environment recorded by aradar sensor that generates the radar recordings, wherein the radar datacomprise the respective distances of these objects to the radar sensor,and the respective angles between these objects and the radar sensorbelonging to these distances, forward propagation of an artificialneural network with the initial training data, obtaining secondcharacteristics of the objects determined by the artificial neuralnetwork in the forward propagation, and obtaining weighting factors forneural connections of the artificial neural network through backwardpropagation of the artificial neural network with the differencesbetween the second characteristics and the initial characteristics.

The present disclosure thus also provides a training process for anartificial neural network, for learning to recognize structures in radardata, and to allocate a semantic meaning to the corresponding objects.

A training system according to the present disclosure is preferably usedfor executing the training process.

The application system according to the present disclosure for a streetvehicle for segmenting radar recordings has an input interface forreceiving radar recordings. The application system also has anevaluation device that is configured to forward propagate an artificialneural network trained in accordance with the training process accordingto the present disclosure with these radar recordings, and to obtain asegmenting of these radar recordings in the forward propagation, and anoutput interface that is configured to output this segmentation.

Street vehicles are land vehicles, which maintain or alter theirdirection of travel by means of friction on a substrate that can bedriven on. In particular, street vehicles are motor-driven vehicles,i.e. motor vehicles, e.g. automobiles or motorcycles.

In contrast to the training system according to the present disclosure,the application system according to the present disclosure contains atrained artificial neural network, and outputs the segmentation. Theapplication system uses distance, speed, azimuth angle and elevationangle as the input channels, instead of color channels, which arenormally red, green and blue. The application system is executed on thebasis of a corresponding training, in order to predict the state of astreet and the course thereof through the position of the vehicle orfrom local reflections on adjacent vehicle. In addition, the applicationsystem recognizes bridges, signs and crash barriers next to the roadway,taking into account the global distribution of the targets. In thismanner, a global scenario is obtained with radar data.

In a preferred embodiment of the present disclosure, the applicationsystem is executed to obtain a virtual image of the environment from theradar data with the artificial neural network, based on thesegmentation.

The scope of the present disclosure also contains an application methodcomprising the following steps: obtaining radar recordings, forwardpropagation of an artificial neural network, trained in accordance withthe training process according to the present disclosure, with theseradar recordings, obtaining a segmentation of these radar recordings inthe forward propagation, and outputting this segmentation.

An application system according to the present disclosure is preferablyused for executing this application method.

According to the present disclosure, a radar sensor for a street vehiclewith an application system according to the present disclosure ispreferred.

The present disclosure also relates to a use of an application systemaccording to the present disclosure for a radar sensor according to thepresent disclosure as a driver assistance system.

This specification will now refer to the figures. In the figures,identical reference symbols indicate identical components, or componentshaving similar functions. The respective relevant components areindicated in the respective figures.

FIG. 1 shows a radar sensor 3. The radar sensor 3 is suitable for use inthe automotive field, for traffic object recognition or for adaptivespeed regulation, in particular as an environment detection sensor of adriver assistance system. The radar 3 complies with the requirements ofEURO NCAP, the European New Car Assessment Program. The radar sensor ispreferably based on the 77 GHz silicon-germanium technology used in theautomotive field. The radar sensor 3 is part of a radar system, whichincludes the radar sensor 3 and a multi-dimensional assembly oftransmitting antennas 36 and receiving antennas 34. FIG. 2a shows a twodimensional antenna assembly with two transmitting antennas 36 and fourreceiving antennas. The resulting eight transmitting/receiving antennapaths are shown in FIG. 2 b.

The radar sensor 3 records radar data of an environment 1. FIG. 9 showsa method for obtaining radar data. The radar data include, inparticular, a distance 5 to an object 4 in the environment 1, an azimuthangle 6, and an elevation angle 7 between the radar sensor 3 and theobject 4. The azimuth angle 6 and the elevation angle 7 indicate thedirection from which an interference signal 31 arrives. The interferencesignal 31 of the object 4 is shown in FIG. 3 for the respectivetransmitting/receiving antenna path, wherein FIG. 3 shows the respectivetemporal course for the frequencies of the interference signals 31.

The object 4 in FIG. 1 is a vehicle. Initial characteristics 2 of thevehicle are the dimensions, position and color of the vehicle. The radardata and the initial characteristics 2 are conveyed to an evaluationdevice 10 via an input interface 11. The evaluation device 10 is anelectronic circuitry that processes input signals from numerousenvironment detection sensors, in particular the radar sensor 3 and animaging sensor 50, with artificial intelligence, in real time, in orderto comprehend what passes by the vehicle, in particular based on radardata. The evaluation device 10 is configured such that it remainsfunctional when exposed to heat, moisture, dust, and other criteria inthe automotive field.

The evaluation device 10 comprises an artificial neural network 20. Theartificial neural network 20 is an assembly of neurons 23, which areinterconnected via respective neural connections 22. The neuralconnections 22 are weighted with respective weighting factors 21. Theartificial neural network 20 computes second characteristics 8 based onthe radar data and the initial characteristics 2. A difference betweenthe second characteristics 8 and the initial characteristics 2, obtainedthrough subtraction, is backward propagated to the artificial neuralnetwork. The artificial neural networks 20 sets the weighting factors 21by means of error minimization in the backward propagation. This meansthat the artificial neural network 20 has learned that a specificstructure in the radar data corresponds to the object 4.

FIG. 4 shows a training system 40. The training system 40 contains theradar sensor 3, the imaging sensor 50 in the form of a camera, and theevaluation device 10 serving as a functionally integrated component. Theradar sensor 3 contains an assembly of seven receiving antennas 34.Based on the different phases of the signals 35 received from the object4 at the individual receiving antennas 34, the direction from which thereceived signal 35 arrives at the radar sensor 3, and thus the directionof the object 4 in relation to the radar sensor 3, is determined bymeans of beamforming. The imaging sensor 50 records the environment 1that the radar sensor 3 records, and obtains the initial characteristics2 of the object 4 by means of semantic image segmentation. The radardata obtained with the radar sensor 3 and the initial characteristics 2are supplied to the evaluation device 10. The evaluation device 10executes the artificial neural network algorithm 20. The artificialneural network 20 obtains second characteristics 8. The secondcharacteristics 8 approximate the initial characteristics 2 in thetraining phase. The training process is shown in FIG. 10.

FIG. 5 shows a distance/speed distribution 33. The distance/speeddistribution 33 is a so-called range-Doppler map, obtained from theinterference signals 31. Speed 9 is plotted on the x-axis. Distance 5 isplotted on the y-axis. The distance/speed distribution 33 indicates afirst maximum at a distance 5 of 65 meters to the radar sensor 3, and aspeed 9 of 0 m/s in relation to the radar sensor 3. A second maximum isindicated at 60 m and 2 m/s.

FIG. 6 shows the results of a beamforming of the distance/speeddistribution 33. The azimuth angle 6 is plotted on the x-axis. Theelevation angle is plotted on the y-axis. A pronounced maximum islocated in an azimuth angle range of ca. −7° to −2°, and an elevationangle range of ca. −2° to +2°.

The four dimensional data range, shown in FIG. 7, containing thedimensions, distance 5, speed 9, azimuth angle 6 and elevation angle 7,is obtained with the distance 5 and the speed 9. The speed 9 dimensionis not indicated in the image. Objects 4 exhibit specific structures inthis data range.

FIG. 8 shows an application system 60. The application system obtainsradar data of a radar recording of the environment via an inputinterface 61. The evaluation device 10 executes an artificial neuralnetwork algorithm 20 that has been trained in accordance with a trainingprocess according to the present disclosure. Data inputs for theartificial neural network 20 comprise radar data. Data outputs comprisesegmentation of the environment. This segmentation is obtained via anoutput interface 62.

The method for obtaining radar data is shown in FIG. 9. Modulatedtransmission signals are transmitted in step V1. The frequencies oftransmission signals are modulated according to the frequency modulatedcontinuous wave method. The interference signals 31 are received in stepV2 based on frequency modulation and distances 5 and relative speeds 9of the objects 4. The distance/speed distribution 33 of the object 4 isobtained from the interference signals 31 in step V3. Azimuth angles 6and elevation angles 7 between interference signals 31, which arrive atthe radar sensor 3 from a direction defined by the azimuth angle 6 andthe elevation angle 7, and the radar sensor 3 are determined in step V4for each value of the distance/speed distribution 33. The radar data inthe form of a four dimensional data range are obtained in step V5 withthe dimensions, distance 5, relative speed 9, azimuth angle 6, andelevation angle 7.

The training process for training the artificial neural network 20 isshown in FIG. 10. The initial training data are obtained in step T1. Theartificial neural network 20 is forward propagated in step T2 with theinitial training data. Second characteristics 8 of the object 4 that aredetermined with the artificial neural network 20 are obtained in theforward propagation in step T3. The weighting factors 21 for the neuralconnections 22 of the artificial neural network 20 are obtained in stepT3 through backward propagation of the artificial neural network 20 withthe differences between the second characteristics 8 and the initialcharacteristics 2.

REFERENCE SYMBOLS

-   1 environment-   2 initial characteristic-   3 radar sensor-   4 object-   5 distance-   6 azimuth angle-   7 elevation angle-   8 second characteristic-   9 relative speed-   10 evaluation device-   11 input interface-   20 artificial neural network-   21 weighting factor-   22 neuron connection-   23 neuron-   31 interference signal-   33 distance/speed distribution-   34 receiving antenna-   35 received signal-   36 transmitting antenna-   40 training system-   50 imaging sensor-   60 application system-   61 input interface-   62 output interface-   V1-V5 method steps-   T1-T4 method steps

1. An evaluation device for obtaining a segmentation of a radarrecording of an environment, comprising: an input interface configuredto obtain initial training data, wherein the initial training datacomprises radar data of the radar recording and initial characteristicsof objects located in the environment recorded with a radar sensor,wherein the radar sensor generates the radar recording, wherein theradar data comprises the respective distances of these objects to theradar sensor and the respective angles between the objects and the radarsensor belonging to these distances, and wherein the evaluation deviceis configured to forward propagate an artificial neural network with theinitial training data, wherein the evaluation device is configured toobtain second characteristics of the objects determined with theartificial neural network in the forward propagation, and wherein theevaluation device is configured to obtain weighting factors for neuralconnections of the artificial neural network through backwardpropagation of the artificial neural network with the differencesbetween the second characteristics and the initial characteristics, andwherein the evaluation device obtains the segmentation of theenvironment from a renewed forward propagation with these radar data. 2.The evaluation device according to claim 1, wherein the evaluationdevice is configured to obtain a virtual image of the environment fromthe radar recording with the artificial neural network based on theinitial characteristics and the segmentation.
 3. The evaluation deviceaccording to claim 1, wherein the initial characteristics arecharacteristics of at least one of the following: at least one street,objects on or adjacent to at least one street, traffic signs, and crashbarriers or other vehicle retention systems.
 4. A method for obtainingradar data comprising the following steps: outputting modulatedtransmission signals, obtaining interference signals based on frequencymodulation and distances and relative speeds of objects at whichtransmitted signals are reflected in relation to a radar sensor, whereinthe interference signals each represent an interference of at least onereflected signal with at least one of the transmitted signals, obtaininga distance/speed distribution of the object based on the interferencesignals determining azimuth angles and elevation angles betweeninterference signals, which arrive at the radar sensor from a directiondefined by the azimuth angle and the elevation angle, and the radarsensor for each value of the distance/speed distribution, and obtainingradar data in the form of a four dimensional data range, comprising thedimensions: distance, relative speed, azimuth angle and elevation angle.5. (canceled)
 6. A training system for obtaining a segmentation of aradar recording of an environment, comprising: a radar sensor with anassembly of receiving antennas, wherein the radar sensor is configuredto receive receiver signals from objects located in the environmentreflecting the transmitted signals; at least one imaging sensorconfigured to obtain initial characteristics of the objects based on animage segmenting of the environment; and an evaluation device configuredto forward propagate an artificial neural network with radar data frominterference signals from respective reflected signals and transmittedsignals and the initial characteristics, wherein the radar datacomprises the respective distances of these objects to the radar sensorand respective angles between the objects and the radar sensor belongingto these distances to obtain second characteristics of the objectsdetermined in the forward propagation of the artificial neural network,and to obtain weighting factors for neural connections of the artificialneural network through backward propagation of the artificial neuralnetwork with the differences between the second characteristics and theinitial characteristics, and wherein the segmentation of the environmentis obtained with a renewed forward propagation with these radar data.7-13. (canceled)